Dodds and his team gathered more than 46 billion words tweeted around the globe, and ran them by Amazon's Mechanical Turk service. They paid a group of volunteers to rate, from one to nine, their sense of the 'happiness' of the ten thousand most common words in English.

Averaging their scores, the volunteers rated, for example, 'laughter' at 8.50, 'food' at 7.44, 'truck' at 5.48, 'greed' at 3.06 and 'terrorist' at 1.30.

The Vermont team then applied these scores to the pool of words collected from Twitter, linking them to date, time and location.

In this way, they say, they can measure happiness at different times and places - more accurately than surveys which depend on self-reported levels of happiness and generally have small sample sizes.

The new approach throws out several interesting insights - we're generall happiest over the weekend, for example, with happiness levels lowest on Mondays and Tuesdays. Over each day, happiness seems to drop from morning to night.

"It's part of the general unraveling of the mind that happens over the course of the day," said Dodds.

While the 'happiest' days are annual holidays like Christmas and Valentine's Day, "all the most negative days are shocks from outside people's routines," Dodds says. Clear drops can be seen with the spread of swine flu, the announcement of the US economic bailout, the tsunami in Japan and even the death of actor Patrick Swayze.

The team's envisaging a tool that could go 'on the dashboard' of policy makers, Dodds says.

The team acknowledges that the technique measures only superficial happiness, rather than a sense of deep satisfaction with life. But they hope that as they expand their data set over a period of years, they'll be able to infer more fundamental feelings, such as individual stability and social engagement.

"By measuring happiness, we're not saying that maximizing happiness is the goal of society," Dodds says. "It might well be that we need to have some persistent degree of grumpiness for cultures to flourish."